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Google DeepMind unveils WeatherNext 3, an hourly global weather AI using live satellite data

Google DeepMind unveils WeatherNext 3, an hourly global weather AI using live satellite data

  • WeatherNext 3 is Google DeepMind's global ensemble weather model that generates forecasts every hour, using raw real-time satellite imagery alongside other observations.
  • The model predicts station-targeted temperature and humidity at 5 km resolution and other surface variables, including wind, at 10 km resolution.
  • Google says WeatherNext 3 covers renewable-energy variables such as radiation and cloud cover for wind and solar farm operations.
  • Google is integrating the model into Search, Maps, and Gemini, and makes forecasts available through BigQuery, Earth Engine, Google Maps Platform, and Cloud Storage.
  • The experimental Weather Lab lets users inspect live global layers, track tropical cyclones, and compare Google's models with traditional meteorological baselines; Google directs users to official agencies for warnings.

Hacker News opinions

I like that the announcement includes an interactive world map with all the forecast layers. The Weather Lab link is .

The paper says the model adds real-time satellite and weather-station observations to the usual reanalysis inputs. That improves resolution, run frequency, and forecast timestep frequency.

I expected this to help energy forecasting versus classic NWP, but I have not seen it deployed much. Has anyone compared these models with conventional NWP in practice?

In Europe, some forecast sites expose these models as another member of an ensemble. A 5 km grid is still coarse, though it is a large improvement over many global models; country-scale models already reach 1-2 km, which matters in mountains and narrow valleys.

Why assume it will be a boon? High-resolution regional NWP with rapid refresh has been standard for more than a decade.

I cannot find a convenient iOS use beyond the Weather Lab web app. Search and Maps show fairly basic weather information.

I found Weather Lab sloppy: it defaults to UTC and imperial units, does not explain initialization time well, and clips hover content in detailed view. It is clearly experimental, but I would not call it easy to use.

I am concerned that reduced initial-condition data could hurt forecast quality. The effects of cuts to weather-observation data are worth watching.

The viewer really needs wind direction as a compass bearing. It matters for wildfires, air quality, and marine conditions, and the model already produces wind as vectors.

Google's weather forecasts are often awful where I live: it can predict no rain while it is pouring and other apps show it. Being badly wrong even one time in ten is enough to damage trust.

I had the same experience and switched to Windy. I check the radar and make my own call because Google's forecast has felt below 50% accurate locally.

I miss Dark Sky's forecast quality. I still wonder what they were doing that made it work so well.

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